Multivariate Shared-Parameter Mixed-Effects Location Scale Model for Analysis of Intensive Longitudinal Data

References

• Ahn, J., Morita, S., Wang, W., and Yuan, Y. (2019), “Bayesian Analysis of Longitudinal Dyadic Data With Informative Missing Data Using a Dyadic Shared-Parameter Model,” Statistical Methods in Medical Research, 28, 70–83. DOI: 10.1177/0962280217715051.
   PubMed Web of Science ®Google Scholar
• Bell, M. L., and Fairclough, D. L. (2014), “Practical and Statistical Issues in Missing Data for Longitudinal Patient Reported Outcomes,” Statistical Methods in Medical Research, 23, 440–459. DOI: 10.1177/0962280213476378.
   PubMed Web of Science ®Google Scholar
• Bolger, N., Davis, A., and Rafaeli, E. (2003), “Diary Methods: Capturing Life as It Is Lived,” Annual Review of Psychology, 54, 579–616. DOI: 10.1146/annurev.psych.54.101601.145030.
   PubMed Web of Science ®Google Scholar
• Cleveland, W. S., Denby, L, and Liu, C. (2002), “Random Location and Scale Effects: Model Building Methods for a General Class of Models,” Computing Science and Statistics, 32, 3–10.
   Google Scholar
• Cursio, J. F., Mermelstein, R. J., and Hedeker, D. (2019), “Latent Trait Shared-Parameter Mixed Models for Missing Ecological Momentary Assessment Data,” Statistics in Medicine, 38, 660–673. DOI: 10.1002/sim.7989.
   PubMed Web of Science ®Google Scholar
• Diggle, P., and Kenward, M. G. (1994), “Informative Drop-Out in Longitudinal Data Analysis,” Journal of the Royal Statistical Society, Series C, 43, 49–93.
   Web of Science ®Google Scholar
• Fleeson, W. (2004), “Moving Personality Beyond the Person-Situation Debate,” Current Directions in Psychological Science, 13, 83–87.
   Web of Science ®Google Scholar
• Follmann, D., and Wu, M. C. (1995), “An Approximate Generalized Linear Model With Random Effects for Informative Missing Data,” Biometrics, 51, 151–168.
   PubMed Web of Science ®Google Scholar
• Heckman, J. (1979), “Sample Selection Bias as a Specification Error,” Econometrica, 47, 153–161.
   Web of Science ®Google Scholar
• Hedeker, D., Berbaum, M., and Mermelstein, R. (2006), “Location-Scale Models for Multilevel Ordinal Data: Between- and Within-Subjects Variance Modeling,” Journal of Probability and Statistical Science, 4, 1–20.
   Google Scholar
• Hedeker, D., and Gibbons, R. D. (2006), Longitudinal Data Analysis, Hoboken, NJ: Wiley.
   Google Scholar
• Hedeker, D., and Mermelstein, R. J. (2007), “Mixed-Effects Regression Models in Heterogeneous Variance: Analyzing Ecological Momentary Assessment Data of Smoking,” in Modeling Contextual Effects in Longitudinal Studies, In T. D. Little, J. A. Bovaird, & N. A. Card, eds., Mahwah, NJ: Lawrence Erlbaum Associates Publishers.
   Google Scholar
• Hedeker, D., Mermelstein, R. J., and Demirtas, H. (2008), “An Application of a Mixed-Effects Location Scale Model for Analysis of Ecological Momentary Assessment (EMA) Data,” Biometrics, 64, 627–634. DOI: 10.1111/j.1541-0420.2007.00924.x.
   PubMed Web of Science ®Google Scholar
• Hertzog, C., and Nesselroade, J. R. (2003), “Assessing Psychological Change in Adulthood: An Overview of Methodological Issues,” Psychology and Aging, 18, 639–657.
   PubMed Web of Science ®Google Scholar
• Kapura, K., Li, X., Blood, E. A., and Hedeker, D. (2015), “Bayesian Mixed-Effects Location and Scale Models for Multivariate Longitudinal Outcomes: An Application to Ecological Momentary Assessment Data,” Statistics in Medicine, 34, 630–651. DOI: 10.1002/sim.6345.
   PubMed Web of Science ®Google Scholar
• Laird, N. M. (1988), “Missing Data in Longitudinal Studies,” Statistics in Medicine, 7, 305–315. DOI: 10.1002/sim.4780070131.
   PubMed Web of Science ®Google Scholar
• Li, X., and Hedeker, D. (2012), “A Three-Level Mixed-Effects Location Scale Model With an Application to Ecological Momentary Assessment (EMA) Data,” Statistics in Medicine, 31, 3192–3210.
   PubMed Web of Science ®Google Scholar
• Liang, K., and Zeger, S. L. (1986a), “Longitudinal Data Analysis Using Generalized Linear Models,” Biometrika, 73, 13–22.
   Web of Science ®Google Scholar
• Liang, K., and Zeger, S. L. (1986b), “Longitudinal Data Analysis for Discrete and Continuous Outcomes,” Biometrics, 42, 121–130.
   PubMed Web of Science ®Google Scholar
• Lin, X., Mermelstein, R. J., and Hedeker, D. (2018a), “A 3-Level Bayesian Mixed Effects Location Scale Model With an Application to Ecological Momentary Assessment Data,” Statistics in Medicine, 37, 2108–2119. DOI: 10.1002/sim.7627.
   PubMed Web of Science ®Google Scholar
• Lin, X., Mermelstein, R. J., and Hedeker, D. (2018b), “A Shared Parameter Location Scale Mixed Effect Model for EMA Data Subject to Informative Missing,” Health Service and Outcomes Research Methodology, 18, 227–243.
   Web of Science ®Google Scholar
• Martin, M., and Hofer, S. M. (2004), “Intraindividual Variability, Change, and Aging: Conceptual and Analytical Issues,” Gerontology, 50, 7–11. DOI: 10.1159/000074382.
   PubMed Web of Science ®Google Scholar
• Nesselroade, J. R. (2004), “Intraindividual Variability and Short-Term Change,” Gerontology, 50, 44–47. DOI: 10.1159/000074389.
   PubMed Web of Science ®Google Scholar
• Nesselroade, J. R., and McCollam, K. M. S. (2000), “Putting the Process in Developmental Processes,” International Journal of Behavioral Development, 24, 295–300.
   Web of Science ®Google Scholar
• Neuhaus, J. M., and Kalbfleisch, J. D. (1998), “Between- and Within-Cluster Covariate Effects in the Analysis of Clustered Data,” Biometrics, 54, 638–645.
   PubMed Web of Science ®Google Scholar
• Penner, L. A., Shiffman, S., Paty, J. A., and Fritzsche, B. A. (1994), “Individual Difference in Intraperson Variability in Mood,” Journal of Personality and Social Psychology, 66, 712–721.
   PubMed Web of Science ®Google Scholar
• Qin, L., Weissfeld, L., Marcus, M. D., Levine, M. D., and Dai, F. (2014), “Modeling Longitudinal Obesity Data With Intermittent Missingness Using a New Latent Variable Model,” Communications in Statistics—Simulation and Computation, 45, 2018–2031.
   Web of Science ®Google Scholar
• Rast, P., and Ferrer, E. (2018), “A Mixed-Effects Location Scale Model for Dyadic Interactions,” Multivariate Behavioral Research, 53, 756–775. DOI: 10.1080/00273171.2018.1477577.
   PubMed Web of Science ®Google Scholar
• Schwartz, J. E., and Stone, A. (2007), The Analysis of Real-Time Momentary Data: A Practical Guide, Oxford: Oxford University Press.
   Google Scholar
• Shiffman, S., Stone, A. A., and Hufford, M. R. (2008), “Ecological Momentary Assessment,” Annual Review of Clinical Psychology, 4, 1–32. DOI: 10.1146/annurev.clinpsy.3.022806.091415.
   PubMed Web of Science ®Google Scholar
• Stone, A. A., and Shiffman, S. (1994), “Ecological Momentary Assessment (EMA) in Behavioral Medicine,” Annals of Behavioral Medicine, 16, 199–202.
   Google Scholar
• Verbeke, G., Fieuws, S., Molenberghs, G., and Davidian, M. (2014), “The Analysis of Multivariate Longitudinal Data: A Review,” Statistical Methods in Medical Research, 23, 42–59. DOI: 10.1177/0962280212445834.
   PubMed Web of Science ®Google Scholar
• Verbeke, G., and Molenberghs, G. (2000), Linear Mixed Models for Longitudinal Data, New York: Springer-Verlag.
   Google Scholar
• Vonesh, E. F., Greene, T., and Schluchter, M. D. (2006), “Shared Parameter Models for the Joint Analysis of Longitudinal Data and Event Times,” Statistics in Medicine, 15, 143–163.
   Google Scholar
• Walls, T. A., and Schafer, J. L. (2006), Models for Intensive Longitudinal Data, New York: Oxford University Press.
   Google Scholar
• Wang, M., Beal, D. J., Chan, D., Newman, D. A., Vancouver, J. B., and Vandenberg, R. J. (2017), “Longitudinal Research: A Panel Discussion on Conceptual Issues, Research Design, and Statistical Techniques,” Work, Aging and Retirement, 3, 1–24.
   Web of Science ®Google Scholar
• Williams, D. R., Liu, S., Martin, S., and Rast, P. (2019), “Bayesian Multivariate Mixed-Effects Location Scale Modeling of Longitudinal Relations Among Affective Traits, States, and Physical Activity,” PsyArXiv.
   Google Scholar